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Benefits of Data Analytics for the Banking and Financial Industry

Every company has a lot of data, especially for the banking industry and financial institutions that get an infinite amount of data flow coming from customer transactions and other data sources. However, the presence of such data has not been maximized because many banks and financial institutions have not implemented certain strategies to manage their data.

This happens because there are still many who think that knowing the meaning and value of data is not easy, especially if the data owned is very large in number. In fact, the right way for banking companies that want to get insights from the data generated is to implement data analytics.

In this article, we will give an answer to the question “what will banking and financial companies get by implementing data analytics?” This answer is spelled out specifically for business people in the banking and financial industries.

Here are the benefits of data analytics for the banking and financial industries:

1. Assess credit risk or potential default

Assessing credit risk or potential default is a top priority for all banking and financial companies, as such activities help companies regulate financial flows and set the value of investments appropriately.

With data analytics, prospective borrowers’ data derived from previous transaction patterns and credit history can help banks and financial institutions assess credit risk more precisely and quickly, so they can pass on the credit process only to customers who have low credit risk or potential for default.

2. Improves performance

With data analytics, banks can assess the extent of the effectiveness of a business process to achieve overall business goals. In addition, the assessment can later help companies quickly improve performance that is not optimal and needs improvement.

3. Determine customer segmentation

With data analytics, banks and financial institutions can leverage historical customer data to determine customer segmentation based on the amount of income, expenses, and credit risk. Later, this segmentation is used to sell services or products according to the needs and capabilities of each customer.

It is very important to distinguish the categories of customers that have the potential to give both profit and disadvantage. So with this solution, banks and financial institutions can distinguish between categories of customers who have the potential to provide benefits and losses if a new service or product is launched – directly increasing the effectiveness of marketing and sales.

4. Avoid fraud

Fraud is a case of cyberattacks that threaten the sustainability of the banking business and financial institutions. By implementing data analytics, banks and financial institutions can analyze the patterns and habits of each customer. Thus, if in the future there is an unusual number of transactions from certain customers, the bank can immediately detect potential fraud and immediately follow up to avoid losses.

5. Knowing the Customer Lifetime Value

Customer Lifetime Value (CLV) is a term used to measure the length of time a customer uses a company’s product or service. CLV is a metric to estimate the total value of customers to a company in a certain period of time. Simply put, CLV is a prediction of the total value of revenue that a company can get from one customer.

By implementing data analytics, banks and financial institutions can find out the CLV correctly. With the increasing accuracy of customer lifetime value, banks can maximize the total value of income that can be obtained from a particular service or product. On the other hand, each customer will also get special treatment, since it is only offered products or services that suit his interests. If you successfully implement this, gaining customer loyalty is not a difficult endeavor to make.

After knowing the benefits of data analytics for the banking and financial industries, as a business person, you can take the next step, which is to implement the right data analytics platform to help manage data in the company environment. Start the implementation process with a technology partner who has credibility and a good reputation in the field of data analytics.

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